Dynamic Distribution of Patients to Medical Facilities in the Aftermath of a Disaster

• 成果类型: Article
• 署名作者: Mills, Alex F.; Argon, Nilay Tanik; Ziya, Serhan
• 署名单位: Indiana University System; Indiana University Bloomington; IU Kelley School of Business; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2017.1695
• 发表日期: 2018
• 页码: 716-732
• 关键词: resource-constrained triage; convex delay costs; c-mu-rule; ambulance location; surge capacity; systems; QUEUE; care; customers; policies

摘要

In the aftermath of a disaster, emergency responders must transport a large number of patients to medical facilities, using limited transportation resources (such as ambulances). Decisions about where to send the patients are typically made in an ad hoc manner by responders on the scene. Using a Markov decision process formulation, we develop two heuristic policies that use limited information such as mean travel times and congestion levels to determine (a) how to allocate ambulances to patient locations and (b) which medical facility should be the destination for those ambulances. In a simulation study, we incorporate patient survival rates and service times for different types of traumatic injuries, and show that the proposed heuristics can provide substantial improvement in the expected number of survivors compared to the common practice of transporting to the nearest facility, even when the decision maker has only limited up-todate information about the system state. In particular, a myopic approach that considers only what is best for the next patient to be transported increases the expected number of survivors in almost all scenarios considered. Using a more sophisticated one-step policy improvement approach provides further improvement when the event involves patients who do not deteriorate rapidly, especially when the transportation is not the bottleneck and the casualties are spread over many locations. We demonstrate the effectiveness of the proposed heuristics on a case study of a hypothetical earthquake, where casualty data is generated using computer software developed by the U.S. government.